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商品＃:
35121987
Building Machine Learning Pipelines: Automating Model Life Cycles with Tensorflow Paperback
商品＃:
35121987
TWD 2472
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產品詳情
- Automate machine learning pipeline using TensorFlow ecosystem
- Cut deployment time from days to minutes
- Utilize components from TensorFlow Extended, Apache Beam, Airflow, and Kubeflow Pipelines
- Work with data using TensorFlow Data Validation and Transform
- Analyze model in detail with TensorFlow Model Analysis, examine fairness and bias
- Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices, learn privacy-preserving techniques
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Pub date | 2020-08-04 |
| Pages | 364 |
| Reading level | Professional & Vocational |
| Subgenre | Computers |
| Edition | 1st Edition |
| Publisher | O'Reilly Media |
| Original languages | English |
| Language | English |
| Binding type | Perfect Binding |
| Assembled product height | 9.19 in (23.3 cm) |
| Assembled product weight | 1.28 lb (580 grams) |
| Bisac subject heading | Computers |
產品描述
Building Machine Learning Pipelines: Automating Model Life Cycles with Tensorflow Paperback
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客戶問答
-
問題:
Who will benefit from this book?
Answer: Data scientists, machine learning engineers, and DevOps engineers -
問題:
What are some of the topics covered in the book?
Answer: Machine learning management lifecycle, data pipelines, model analysis, fairness and bias in model performance, privacy-preserving machine learning techniques -
問題:
What tools can be used for orchestration?
Answer: Apache Beam, Apache Airflow, and Kubeflow Pipelines
Hannes Hapke; Catherine Nelson All Books Editorial Review
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優點
- Comprehensive guide on ML pipelines
- Practical examples and projects
- Clear explanations for beginners
- Great for automating processes
- Up-to-date information on TensorFlow
缺點
- Some minor typographical errors.
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TWD 2472
立即訂購並活動它 週四, 七月 09
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特色和優勢
- Automate a machine learning pipeline
- Cut deployment time from days to minutes
- Focus on developing new models rather than maintaining legacy systems